Technological learning in energy–environment–economy modelling: A survey

By S. Kahouli-Brahmi. 2008

265 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/44c482d2f24a5e8a71a0bc4f5afddf68c7fae7c8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

From fossil fuels to renewables: An analysis of long-term scenarios considering technological learning (2019, methodology): “Quantification of these learning patterns is presented in the literature using so-called one-factor and multi-factor learning curves (Kahouli-Brahmi, 2008; Rubin et al., 2015).”
Interim monitoring of cost dynamics for publicly supported energy technologies (2009, background): “And for those studies that use experience curves to represent technological change, assumptions about LRs are important (Rubin et al., 2004; Kahouli-Brahmi, 2008).”
Market dynamics, innovation, and transition in China's solar photovoltaic (PV) industry: A critical review (2017, background): “Learning-by-doing also includes additional mechanisms, such as learning-by-operating and learning-by-implementing [15,57,58].”
Relating R&D and Investment Policies to CCS Market Diffusion Through Two-Factor Learning (2013, methodology): “Within power generation, learning curves are frequently used for economic and policy analysis; Kahouli-Brahmi (2008), for example, provides a survey with 14 examples.”
Power capacity expansion planning considering endogenous technology cost learning (vol 204, pg 831, 2017) (2017, methodology): “However, economic causality analyses provide evidence that technological change and diffusion are indeed dependent variables [24, 36].”
A review of learning rates for electricity supply technologies (2015, background): “Overall rates span a very large range, from –11% to 35% (Edenhofer et al., 2011; Ibenholt, 2002; Junginger et al., 2005; Kahouli-Brahmi, 2008; Lindman and Söderholm, 2012; McDonald 0% 2% 4% 6% 8% 10% 12% 1980 1985 1990 Le ar ni ng R at e Period McDonald and Schrattenholzer,…”
The Role of R&D and Technology Diffusion in Climate Change Mitigation: New Perspectives Using the Witch Model (2009, unknown): “In particular, learning rate estimates vary considerably for technologies used in the electricity sector, from about 1% to over 40%, around a central estimate of about 20% (Jamasab and Köhler, 2007; Kahouli-Brahmi, 2008).”
Mitigation Costs in a Globalized World: Climate Policy Analysis with REMIND-R (2010, background): “The survey given by Kahouli-Brahmi (2008) indicated that most recently major progress was made in modeling endogenous and induced technological change (see also Löschel, 2002; Edenhofer et al., 2006).”